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Illustration: Aïda Amer/Axios

AI is speeding up the discovery of the structure of proteins that drive biological processes across organisms.

Why it matters: If researchers can predict what shape a protein will take, they can better understand how it works — and potentially target medicines for proteins that cause disease or create antibiotics that can disable resistant bacteria's proteins.

The big picture: Determining the structure of proteins is typically done through painstaking experiments involving crystallizing proteins and analyzing them with X-rays. That's yielded the shapes of a small fraction of proteins in humans.

  • But new machine-learning systems like AlphaFold and RoseTTAFold are making a "once in a generation" advance and have been able to speed up the discovery process greatly.
  • "If you really want to understand how biology works at the molecular level — and that is really where it works, with little machines interacting with each other — you need to know the shape of the protein molecules," said John Moult, a structural biologist at the University of Maryland, Shady Grove.
  • "For a long time we've known the sequences at the DNA level of all the genes, and those sequences dictate what these shapes are. But the way in which you can get from 'here's the sequence' to 'what's the detailed shape' has been an outstanding computational problem for more than 50 years," Moult told Axios.
  • Moult is also co-founder of the Critical Assessment of Structure Prediction, or CASP, which is a challenge that's been run for 25 years to test modeling programs predicting protein structures.

The latest: Google DeepMind's AlphaFold2 system is able to issue a “good prediction" of protein structures about 95% of the time, scientists said at a joint press conference announcing DeepMind and the European Molecular Biology Laboratory (EMBL)'s collaboration.

  • Using AlphaFold 2, scientists were able to generate 3D models of 350,000 proteins, 36% of which have a "high confidence," they said. The researchers have opened these structures to use via the AlphaFold database in an "effort to move the science forward," said DeepMind's Demis Hassabis, co-author of the paper in Nature.
  • Another co-author, Centre for Enzyme Innovation director John McGeehan, said they had DeepMind test seven enzymes in their experiments to break down plastics, two of which they had already found experimental structures. DeepMind quickly confirmed the two structures and gave further information on the others, too.
  • "It's one of those moments, to be honest, where the hair stood up at the back of my neck," McGeehan said. "The structures that [GoogleMind] produced were identical to our crystal structures. In fact, they contained even more information than the crystal structures were able to provide [using traditional methods]. ... The acceleration to our project is multiple years."

The interpretation of rare genetic mutations is another area AI is expected to target, said EMBL deputy director and co-author Ewan Birney.

  • "This is a very practical problem in clinical genetics, where you have a suspected series of mutations or changes in an affected child, and you want to try and work out which one is likely to be the reason why your child has got a particular genetic disease."

Context: The AlphaFold2 findings follow last week's announcement that the University of Washington created a neural network, RoseTTAFold, to determine protein structures and published several openly via GitHub.

What's next: "This is just the first clear demonstration of  the power of AI in biology," Moult said.

  • "There are obvious next things that are likely to happen, to do with how proteins interact and drug development."
  • "Our understanding of protein structures and associated biology will take a big leap forward as people build on these resources," Moult said.

Go deeper: An AI answers one of biology's biggest problems

Go deeper

Updated Sep 29, 2021 - Axios Events

Watch: A conversation on how AI is revolutionizing across industries

On Wednesday, September 29, Axios future correspondent Bryan Walsh discussed how companies and governments are integrating AI to modernize efficient systems in society, featuring Pittsburgh Mayor William Peduto and Global GM (Smart Cities & Transportation) for IOT Solutions at Intel Sameer Sharma.

Mayor William Peduto spoke about how Pittsburgh’s universities have aided in the city’s R&D for AI, the importance of standards creation for algorithms, and how Pittsburgh has adopted AI technology into some of the city’s management systems.

  • On crafting standards for regulating AI security: “One of the industries that I think that really needs to be looked at and needs to start at a local level is how we can incorporate AI for good, in order to be able to understand what the rules and regulations should be for artificial intelligence as we consume it on a daily basis through social media and other means.”
  • On successful AI integration in Pittsburgh’s transportation system: “We began creating traffic signals that actually could learn, traffic signals that were created with algorithms and sensors that could make our streets safer, that could reduce idling time by 34%.”

Sameer Sharma explained how cities can harness AI to enhance public safety, the future of smart city initiatives, and how the pandemic shifted many cities’ public services to digital platforms.

  • On using AI to analyze vast amounts of data: “When you have this data tsunami and we are already in the middle of it, how do you make sense of it? That’s where the power of AI at the edge and in the cloud comes in, and it needs to be connected to an intelligent network.”
  • On how COVID-era digitization could boost smart city initiatives: “COVID was a very tough experience for us as a global community, but if there is one silver lining, it is that years of debate and discussion about digitization, digital transformation, has converted into months of action. And you clearly saw cities that were thinking ahead and had built some resilience and flexibility into their infrastructure were able to pivot quickly and provide these new services.”

Axios SVP of Product and Technology Melanie Colton hosted a View from the Top segment with Beyond Limits CEO and Founder AJ Abdallat, who discussed how AI is being applied to existing knowledge to solve complex problems.

  • “What we’re seeing and what we’re doing here at Beyond Limits is we see the evolution as AI will center around cognitive reasoning. This will enhance the numeric AI applications. This hybrid approach to AI allows us to really solve more complex problems, more challenging problems, by combining the data-driven approach with embedded human knowledge.”

Thank you Beyond Limits for sponsoring this event.

Stephen Breyer formally announces retirement from Supreme Court

Photo: Andrew Harnik/AP

Justice Stephen Breyer on Thursday sent a letter to President Biden formally announcing his retirement from the Supreme Court.

State of play: Breyer said his retirement will take effect when the court "rises for the summer recess (typically late June or early July) assuming that by then my successor has been nominated and confirmed."

COVID created an epic U.S. trade gap

Chart: Axios Visuals. Data: Census Bureau/Bureau of Economic Analysis

The details of the blockbuster fourth quarter GDP report released Thursday morning tell a vivid story of how the underpinnings of the world economy have been reshaped by the pandemic.

One example: In the arithmetic around U.S. economic output, trade acted as a more severe drag last year than it has in a generation.

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